Quantitative Radiomics Approach to Assess Acute Radiation Dermatitis in Breast Cancer Patients

We applied a radiomics approach to skin surface images to objectively assess radiodermatitis in patients undergoing radiotherapy for breast cancer. A prospective cohort study of 20 patients was conducted. Skin surface images in normal, polarised, and UV modes were acquired using a skin analysis device before starting radiotherapy (‘before RT’), 7 days after the first treatment ('RT_D7'), on ‘RT_D14’, and 10 days after radiotherapy ended (‘after RT_D10’). Eighteen types of radiomic feature ratios were calculated. We measured skin doses in ipsilateral breasts using OSLDs on the first day of radiotherapy. Clinical evaluation of acute radiodermatitis was performed using the RTOG scoring criteria on ‘RT_D14’ and ‘after RT_D10’. Several statistical analysis methods were used to test the performance of radiomic features as indicators of radiodermatitis evaluation. As the skin was damaged by radiation, the energy for normal mode and sum variance for polarised and UV modes decreased significantly for ipsilateral breasts. The radiomic feature ratios at ‘RT_D7’ had strong correlations to skin doses and those at ‘RT_D14’ and ‘after RT_D10’ with statistical significance. The energy for normal mode and sum variance for polarised and UV modes demonstrated the potential to evaluate and predict acute radiation, which assists in its appropriate management.

A Hybrid Image Enhancement Algorithm for Effective Concrete Surface Crack Classification

Huge number of images are acquired and analysed every day for a range of applications in civil infrastructure. One such application is the identification of cracks in concrete surface images, which is a challenge owing to their low contrast and resolution, blurriness, noise and information loss. Existing image enhancement algorithms improve either contrast or resolution to a rather limited extent. This paper proposes a Hybrid Image Enhancement (HIE) algorithm to improve both the contrast and resolution of concrete surface images using the Wavelet transform and Singular Value Decomposition (SVM). The enhanced concrete surface crack images are classified into specific crack types. The classification comprises preprocessing, crack detection, feature extraction and crack classification. The images are initially preprocessed using the Wiener filter to remove blurriness, following which cracks are detected using morphological operations and discontinuities in the segmented crack regions eliminated using the K-Dimensional Tree algorithm. Features are extracted from the segmented regions using statistical and geometric features. The image is classified thereafter into specific crack types using algorithms from three different neural network, kernel and tree based categories. The proposed HIE algorithm is validated using quantitative metrics and the results obtained are compared with those from State-of-the-Art methods and datasets. The results have shown that the HIE algorithm offers significantly improved accuracy of between 6% and 10% in the classification of concrete surface images.

Review of Quantitative Imaging for Objective Assessment of Fat Grafting Outcomes in Breast Surgery

This article presents a comprehensive review of the use of quantitative measurements based on breast images to assess fat retention, breast aesthetics, and fat necrosis after autologous fat grafting. Breast volume measured from MRI and 3-dimensional surface images is widely used as a measure of fat retention. Breast aesthetics are assessed from a combination of anthropometric (distance and contour) measurements on breast surface images. Examination of radiologic images (ultrasonography, mammography, and MRI) is utilized to assess fat necrosis. The article discusses implementation guidelines for objective outcome assessment to support robust quantification and enable investigations of fat grafting efficacy. Level of Evidence: 4